Method, electronic device, and computer program product for processing information

ABSTRACT

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for processing information. According to an example embodiment, the method includes: acquiring a service request record set, each service request record in the service request record set relating to a problem encountered by a user when the user is provided with a service and a solution to the problem; constructing a language model based on a first subset in the service request record set and an initial model, the initial model being trained using a predetermined corpus and configured to determine vector representations of words and sentences in the corpus; and constructing a classification model based on a second subset in the service request record set and the language model, the classification model being capable of determining a solution to a pending problem, and the first subset being different from the second subset.

RELATED APPLICATION(S)

The present application claims priority to Chinese Patent ApplicationNo. 202010621888.6, filed Jun. 30, 2020, and entitled “Method,Electronic Device, and Computer Program Product for ProcessingInformation,” which is incorporated by reference herein in its entirety.

FIELD

Embodiments of the present disclosure generally relate to the field ofinformation processing, and in particular, to a method, an electronicdevice, and a computer program product for processing information.

BACKGROUND

With the development of information technologies, the number ofcompanies providing information services is increasing. Especially forcompanies that provide large-scale and complex information services,users are likely to encounter a variety of problems when using theinformation services provided by such companies. The problems may becaused by software defects, hardware or network problems, or operationalerrors. To this end, a team of technical support engineers needs to dealwith a large number of service requests relevant to problem reporting.However, it is a difficult task to quickly and efficiently find asolution or relevant information of a solution that may solve a problemreported in a service request.

SUMMARY

A method, an electronic device, and a computer program product forprocessing information are provided in embodiments of the presentdisclosure.

In a first aspect of the present disclosure, a method for processinginformation is provided. The method includes: acquiring a servicerequest record set, each service request record in the service requestrecord set relating to a problem encountered by a user when the user isprovided with a service and a solution to the problem; constructing alanguage model based on a first subset in the service request record setand an initial model, the initial model being trained using apredetermined corpus and configured to determine vector representationsof words and sentences in the corpus; and constructing a classificationmodel based on a second subset in the service request record set and thelanguage model, the classification model being capable of determining asolution to a pending problem, and the first subset being different fromthe second subset.

In a second aspect of the present disclosure, an electronic device isprovided. The device includes at least one processing unit and at leastone memory. The at least one memory is coupled to the at least oneprocessing unit and stores instructions configured to be executed by theat least one processing unit. When executed by the at least oneprocessing unit, the instructions cause the device to perform actionsincluding: acquiring a service request record set, each service requestrecord in the service request record set relating to a problemencountered by a user when the user is provided with a service and asolution to the problem; constructing a language model based on a firstsubset in the service request record set and an initial model, theinitial model being trained using a predetermined corpus and configuredto determine vector representations of words and sentences in thecorpus; and constructing a classification model based on a second subsetin the service request record set and the language model, theclassification model being capable of determining a solution to apending problem, and the first subset being different from the secondsubset.

In a third aspect of the present disclosure, a computer program productis provided. The computer program product is tangibly stored on anon-transitory computer-readable medium and includes machine-executableinstructions; and when executed, the machine-executable instructionscause a machine to perform any steps of the method described accordingto the first aspect of the present disclosure.

This Summary is provided in a simplified form to introduce the selectionof concepts, which will be further described in the Detailed Descriptionbelow. The Summary is neither intended to identify key features oressential features of the present disclosure, nor intended to limit thescope of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

By description of example embodiments of the present disclosure in moredetail with reference to the accompanying drawings, the above and otherobjectives, features, and advantages of the present disclosure willbecome more apparent. In the example embodiments of the presentdisclosure, the same reference numerals generally represent the samecomponents.

FIG. 1 is a schematic diagram of an example environment in which someembodiments of the present disclosure can be implemented;

FIG. 2 is a schematic diagram of an example of service request recordsaccording to some embodiments of the present disclosure;

FIG. 3 is a schematic diagram of an example of a process forconstructing an initial model, a language model, and a classificationmodel according to some embodiments of the present disclosure;

FIG. 4 is a schematic diagram of an example of a base model according tosome embodiments of the present disclosure;

FIG. 5 is a schematic diagram of an example of a process forconstructing a language model according to some embodiments of thepresent disclosure;

FIG. 6 is a schematic diagram of an example of a process forconstructing a classification model according to some embodiments of thepresent disclosure;

FIG. 7 is a schematic diagram of an example of comparison between aclassification model and a bidirectional long short-term memory (BiLSTM)language model according to some embodiments of the present disclosure;

FIG. 8 is a flowchart of a method for constructing a classificationmodel according to some embodiments of the present disclosure; and

FIG. 9 is a schematic block diagram of an example device that can beconfigured to implement an embodiment of content of the presentdisclosure.

In the accompanying drawings, the same or corresponding numeralsrepresent the same or corresponding parts.

DETAILED DESCRIPTION

Illustrative embodiments of the present disclosure will be described inmore detail below with reference to the accompanying drawings. Althoughthe illustrative embodiments of the present disclosure are shown in theaccompanying drawings, it should be understood that the presentdisclosure can be implemented in various forms and should not be limitedby the embodiments set forth herein. Rather, these embodiments areprovided to make the present disclosure more thorough and complete andto fully convey the scope of the present disclosure to those skilled inthe art.

The term “include” and its variants as used herein indicate open-endedinclusion, for example, “including, but not limited to.” Unlessspecifically stated, the term “or” indicates “and/or.” The term “basedon” indicates “based at least in part on.” The terms “an exampleembodiment” and “an embodiment” indicate “at least one exampleembodiment.” The term “another embodiment” indicates “at least oneadditional embodiment.” The terms “first,” “second,” and the like mayrefer to different or identical objects. Other explicit and implicitdefinitions may also be included below.

Conventionally, as described above, a team of technical supportengineers of a company providing information services needs to deal witha large number of service requests relevant to problem reporting. Inorder to improve efficiency and provide assistance to other engineers,problem-solving engineers may generally summarize and record processesof fixing and solving common problems. For this reason, when a problemreported in a service request is fixed and solved, information about asolution to the problem may be stored in a service request record. Insome cases, processes for fixing and solving various types of problemsmay be formed as a variety of knowledge bases.

However, in most teams of technical support engineers, team membersgenerally have different levels of experience, and junior engineers aremuch less efficient than senior engineers. For this reason, it is adifficult task to help engineers to quickly and efficiently find asolution or relevant information of a solution that may solve a problemreported in a service request.

According to an example embodiment of the present disclosure, animproved solution is proposed. In the solution, a service request recordset may be acquired, and a language model may be constructed based on afirst subset in the service request record set and an initial model.Each service request record in the service request record set relates toa problem encountered by a user when the user is provided with a serviceand a solution to the problem. The initial model is trained using apredetermined corpus and is configured to determine vectorrepresentations of words and sentences in the corpus. Further, aclassification model may be constructed based on a second subset in theservice request record set and the language model, the second subsetbeing different from the first subset. The classification model iscapable of determining a solution to a pending problem.

In this way, this solution can quickly and efficiently determinesolutions to problems reported in service requests, thereby improvingthe efficiency of solving the problems by engineers and improving theuser experience.

In the following, specific examples of this solution will be describedin more detail with reference to FIG. 1 to FIG. 9. FIG. 1 is a schematicdiagram of example environment 100 according to some embodiments of thepresent disclosure. Environment 100 includes computing device 110.Computing device 110 may acquire service request record set 120 andinitial model 130. Service request record set 120 includes a pluralityof service request records. Each service request record includes aproblem encountered by a user when the user is provided with a serviceand a solution to the problem. In some embodiments, the solution mayalso be a knowledge base including the solution or relevant knowledge ofthe solution.

FIG. 2 is a schematic diagram of an example of service request record200 according to some embodiments of the present disclosure. As shown inFIG. 2, service request record 200 includes abstract 210 of the problem,detailed description 220 of the problem, and identification 230 of asolution to the problem. For example, abstract 210 of the problem anddetailed description 220 of the problem may each be a combination ofunstructured text including a plurality of words, such as customerdescriptions, system logs, memory dump files, performance statisticalinformation, and stack traces. In some cases, service request record 200stores valid identification 230 of a solution, for example, a name, anumber, an index, an address, or the like of the solution. However, insome cases, identification 230 of the solution stored in service requestrecord 200 may be invalid and may be represented by a predeterminedvalue (for example, “NA”).

Referring back to FIG. 1, initial model 130 may be obtained by traininga base model by using a predetermined corpus, and is configured todetermine vector representations of words and sentences in the corpus.The base model may be implemented through a transformer model, arecurrent neural network (RNN), or a long short-term memory (LSTM)network.

Computing device 110 may construct language model 140 based on a subset(hereinafter referred to as a “first subset”) in service request recordset 120 and initial model 130. Further, computing device 110 mayconstruct classification model 150 based on another subset (hereinafterreferred to as a “second subset”) in service request record set 120 andlanguage model 140. Classification model 150 is capable of determining asolution to a pending problem. Thus, when receiving a new servicerequest, computing device 110 may apply the new service request toclassification model 150 to quickly and efficiently determine a solutionof the pending problem that is reported in the new service request.Therefore, the problem solving efficiency and user experience can beimproved.

In the following, operations performed by computing device 110 aredescribed in more detail with reference to FIG. 3 to FIG. 6. FIG. 3 is aschematic diagram of an example of process 300 for constructing aninitial model, a language model, and a classification model according tosome embodiments of the present disclosure. For example, process 300 maybe performed by computing device 110 as shown in FIG. 1. It should beunderstood that process 300 may further include additional steps notshown and/or may skip the steps shown, and that the scope of the presentdisclosure is not limited in this respect.

As described above, computing device 110 may acquire initial model 130.Initial model 130 may be constructed 314 by training base model 310 byusing predetermined corpus 312, and may be configured to determinevector representations of words and sentences in predetermined corpus312. For example, initial model 130 may output a vector representing apredetermined size (for example, 50 dimensions) of words.

In some embodiments, construction 314 of initial model 130 may beperformed by computing device 110. Alternatively, construction 314 mayalso be performed by other subjects. Base model 310 may be implementedthrough a transformer model, a recurrent neural network (RNN), or a longand short-term memory (LSTM) network. Predetermined corpus 312 may be acorpus with a large number of words and sentences, for example, aWikipedia corpus, a book corpus, and the like.

FIG. 4 is schematic diagram 400 of an example of base model 310according to some embodiments of the present disclosure. For example,base model 310 is implemented through the transformer model, and basemodel 310 includes a plurality of stacked encoders (for example, 12) ofthe transformer model. The transformer model is an attention mechanismfor learning a contextual relationship between words in text. The numberof the stacked encoders determines the number of parameters of basemodel 310. Since the encoder reads a plurality of words, it may beconsidered bidirectional. This feature enables base model 310 to learn acontext of a word based on a surrounding environment of the word (forexample, other words near the word).

As shown in FIG. 4, the input to base model 310 is a plurality of words410 as tags. It should be understood that the number of words 410 shownin FIG. 4 is merely an example, and in each iteration (for example, ineach epoch), any suitable number of words may be input, for example, 128words, 256 words, 512 words, and so on.

In some embodiments, input words 410 may begin with a predetermined word(for example, “[CLS]”), and sentences may also be separated by apredetermined word (for example, “[SEP]”) (not shown). Words 410 arefirst converted into vectors and then processed in stacked encoders. Theoutput from base model 310 is a plurality of vector representations 420of a predetermined size, and each of the plurality of vectorrepresentations 420 corresponds to a word 410 at the same position. Avector representation corresponding to a predetermined word (forexample, “[CLS]”) at the beginning may be recognized as a vectorrepresentation of a sentence.

Referring back to FIG. 3, in order to train base model 310 to initialmodel 130 by using predetermined corpus 312, during construction 314stage, two strategies, namely, masked language model (MLM) and nextsentence prediction (NSP), may be used.

In the MLM strategy, some of a plurality of words are replaced with apredetermined word (for example, “[MASK]”), and the plurality of wordsare input to base model 310 for training. In some embodiments, apredetermined proportion (for example, 15%) of the words may bereplaced. Such a replacement process may also be considered as a processof masking words by using a predetermined word. Base model 310 mayattempt to predict masked words based on a context provided by otherwords that are not masked. For example, a probability that each word incorpus 312 is a masked word may be calculated by using a softmaxfunction. Base model 310 is trained by adjusting parameters of basemodel 310 so that the predicted words are close to actual masked words.

In addition, in the NSP strategy, base model 310 receives pairedsentences as an input, and attempts to predict whether a second sentencein the paired sentences is a subsequent sentence in a context in which afirst sentence is located. To this end, it needs to add a classificationlayer to an output generated for the predetermined word (for example,“[CLS]”) at the beginning by base model 310, so as to predict whetherthe second sentence is a subsequent sentence (for example, classified as“true” or “false”). Base model 310 is trained by adjusting parameters ofbase model 310 so that a prediction result is close to an actual result.

The construction of initial model 130 has been described above. Itshould be understood that since the initial model 130 is constructedusing predetermined corpus 312 with a large number of words andsentences, in the subsequent process, language model 140 may beconstructed and classification model 150 may be generated by using onlya service request record set with fewer words and sentences to fine-tuneinitial model 130. Thus, the accuracy of the solution determined byclassification model 150 is improved.

Computing device 110 may acquire initial model 130 and service requestrecord set 120, and construct 324 language model 140 based on a firstsubset in service request record set 120 and initial model 130.Specifically, in some embodiments, computing device 110 may determinethe first subset from service request record set 120. An identificationof a solution in each service request record in the first subset is aninvalid identification (for example, “NA”).

Computing device 110 may divide, based on a generation time of eachservice request record in the first subset, the first subset into firstgroup of service request records 322 configured to construct 324language model 140 and second group of service request records 326configured to evaluate 328 language model 140. For example, servicerequest records whose generation time is earlier than a threshold timemay be used as first group of service request records 322, and servicerequest records whose generation time is later than the threshold timemay be used as second group of service request records 326.

Accordingly, computing device 110 may construct 324 language model 140based on initial model 130 by using first group of service requestrecords 322, and may evaluate 328 language model 140 by using secondgroup of service request records 326. For example, in order to construct324 language model 140, computing device 110 may replace at least oneword in each service request record in first group of service requestrecords 322 with at least one predetermined word (for example, “[MASK]”)to generate a first group of replaced service request records. Inaddition, computing device 110 may construct language model 140 byapplying the first group of replaced service request records to initialmodel 130. In some embodiments, a predetermined proportion (for example,15%) of the words may be replaced.

Such a replacement process may also be considered as a process ofmasking words by using a predetermined word. Initial model 130 mayattempt to predict masked words based on a context provided by otherwords that are not masked. For example, a probability that each word infirst group of service request records 322 is a masked word may becalculated by using a softmax function. Language model 140 isconstructed 324 by adjusting parameters of initial model 130 so that thepredicted words are close to actual masked words.

FIG. 5 is schematic diagram 500 of an example of a process forconstructing 324 language model 140 according to some embodiments of thepresent disclosure. The input to initial model 130 is a plurality ofwords 520 as tags. Compared with actual words 510, the word“improvisation” is replaced with the predetermined word “[MASK].” Itshould be understood that the number of words 520 shown in FIG. 5 ismerely an example, and in each iteration (for example, in each epoch),any suitable number of words may be input, for example, 128 words, 256words, 512 words, and so on.

Input words 520 may begin with a predetermined word (for example,“[CLS]”), and sentences may also be separated by a predetermined word(for example, “[SEP]”) (not shown). In some embodiments, words 520 maybe converted into vectors and then processed in initial model 130. Theoutput 530 of initial model 130 is a probability that each word in firstgroup of service request records 322 is a masked word. As shown in FIG.5, a probability that the word “Aardvark” is a masked word is 0.1%, aprobability that the word “Improvisation” is a masked word is 10%, and aprobability that the word “Zyzzyva” is a masked word is 0%.

Furthermore, in order to evaluate 328 language model 140, computingdevice 110 may replace at least one word in each service request recordin second group of service request records 326 with at least onepredetermined word (for example, “[MASK]”) to generate a second group ofreplaced service request records. In addition, computing device 110 mayapply the second group of replaced service request records to languagemodel 140 to determine at least one prediction result of the at leastone word. Computing device 110 may determine a probability that at leastone prediction result matches at least one word, and evaluate 328language model 140 based on the probability.

Alternatively, computing device 110 may use a perplexity as an index forevaluating 328 language model 140. A lower perplexity indicates a betterperformance of the model. Through the perplexity index, it may be foundthat, compared with initial model 130, the perplexity of language model140 is significantly lower than that of initial model 130.

The construction and evaluation of language model 140 have beendescribed above. Computing device 110 may further construct 334 andevaluate 338 classification model 150 based on language model 140. Insome embodiments, computing device 110 may determine the second subsetfrom service request record set 120. An identification of a solution ineach service request record in the second subset is a valididentification. The valid identification may indicate a name, a number,an index, an address, and the like of the solution.

Computing device 110 may divide, based on a generation time of eachservice request record in the second subset, the second subset intothird group of service request records 332 configured to constructclassification model 150 and fourth group of service request records 336configured to evaluate classification model 150. For example, servicerequest records whose generation time is earlier than a threshold timemay be used as third group of service request records 332, and servicerequest records whose generation time is later than the threshold timemay be used as fourth group of service request records 336.

Accordingly, computing device 110 may construct 334 classification model150 by using third group of service request records 332, and mayevaluate 338 language model 140 by using fourth group of service requestrecords 336. FIG. 6 is schematic diagram 600 of an example of a processfor constructing 334 classification model 150 according to someembodiments of the present disclosure.

For example, in order to construct 334 classification model 150,language model 140 receives a plurality of words 610 from a servicerequest record in third group of service request records 332 as aninput, and attempts to predict solution 620 for the service requestrecord. It should be understood that the number of words 610 shown inFIG. 6 is merely an example, and in each iteration (for example, in eachepoch), any suitable number of words may be input, for example, 128words, 256 words, 512 words, and so on.

To this end, in some embodiments, a classification layer may be added toan output generated for the predetermined word (for example, “[CLS]”) atthe beginning by language model 140, so as to predict a solution.Classification model 150 is constructed 334 by adjusting parameters oflanguage model 140 so that a prediction result is close to an actualresult.

Further, in order to evaluate 338 classification model 150, computingdevice 110 may apply fourth group of service request records 336 toclassification model 150 to obtain a predicted solution, and determine aprobability that the predicted solution matches an actual solutionindicated by the identification. Thus, computing device 110 may evaluateclassification model 150 based on the probability.

Alternatively, computing device 110 may use a perplexity as an index forevaluating 338 classification model 150. For example, a perplexity ofthe top one prediction result and a perplexity of top N predictionresults may be adopted, where N is a natural number greater than 1. Theperplexity of the top one prediction result indicates a maximumprobability of an output by classification model 150. The maximumprobability represents a confidence of the predicted solution. Theperplexity of the top N prediction results indicates a frequency atwhich an actual solution falls into predicted solutions with top Nmaximum probabilities output by classification model 150.

Through the perplexity index, it may be found that the accuracy ofclassification model 150 is significantly higher than that of abidirectional long short-term memory (BiLSTM) language model. FIG. 7 isa schematic diagram of an example of comparison 700 betweenclassification model 710 and a bidirectional long short-term memory(BiLSTM) language model 720 according to some embodiments of the presentdisclosure. As shown in FIG. 7, in terms of the accuracy of top Nprediction results, where 1≤N≤20, classification model 710 exhibits alower perplexity, and thus a higher accuracy, than the bidirectionallong-term short-term memory (BiLSTM) language model 720.

In this way, this solution can quickly and efficiently determinesolutions to problems reported in service requests, thereby improvingthe efficiency of solving the problems by engineers and improving theuser experience.

FIG. 8 is a flowchart of method 800 according to some embodiments of thepresent disclosure. For example, method 800 may be performed bycomputing device 110 as shown in FIG. 1. It should be understood thatmethod 800 may further include additional steps not shown and/or mayskip the steps shown, and that the scope of the present disclosure isnot limited in this respect.

In 810, computing device 110 acquires a service request record set. Eachservice request record in the service request record set relates to aproblem encountered by a user when the user is provided with a serviceand a solution to the problem. In 820, computing device 110 constructs alanguage model based on a first subset in the service request record setand an initial model. The initial model is trained by using apredetermined corpus and is configured to determine vectorrepresentations of words and sentences in the corpus. In 830, computingdevice 110 constructs a classification model based on a second subset inthe service request record set and the language model. Theclassification model is capable of determining a solution to a pendingproblem. The first subset is different from the second subset.

In some embodiments, each service request record in the service requestrecord set includes: an abstract of the problem including a plurality ofwords, a detailed description of the problem including a plurality ofwords, and an identification of a solution to the problem.

In some embodiments, in order to construct the language model, computingdevice 110 may determine the first subset from the service requestrecord set. An identification of a solution in each service requestrecord in the first subset is an invalid identification. Computingdevice 110 may divide, based on a generation time of each servicerequest record in the first subset, the first subset into a first groupof service request records configured to construct the language modeland a second group of service request records configured to evaluate thelanguage model. Thus, computing device 110 may construct the languagemodel based on the initial model by using the first group of servicerequest records.

In some embodiments, in order to construct the language model based onthe initial model, computing device 110 may replace at least one word ineach service request record in the first group of service requestrecords with at least one predetermined word to generate a first groupof replaced service request records. Thus, computing device 110 mayconstruct the language model by applying the first group of replacedservice request records to the initial model.

For example, computing device 110 may replace at least one word in eachservice request record in the second group of service request recordswith at least one predetermined word to generate a second group ofreplaced service request records. Computing device 110 may apply thesecond group of replaced service request records to the language modelto determine at least one prediction result of the at least one word.Computing device 110 may determine a probability that at least oneprediction result matches at least one word. Thus, computing device 110may evaluate the classification model based on the probability.

In some embodiments, in order to construct the language model, computingdevice 110 may determine the second subset from the service requestrecord set. An identification of a solution in each service requestrecord in the second subset is a valid identification. Computing device110 may divide, based on a generation time of each service requestrecord in the second subset, the second subset into a third group ofservice request records configured to construct the classification modeland a fourth group of service request records configured to evaluate theclassification model. Thus, computing device 110 may construct theclassification model by using the third group of service requestrecords.

In some embodiments, computing device 110 may apply the fourth group ofservice request records to the classification model to obtain apredicted solution. Computing device 110 may determine a probabilitythat the predicted solution matches the solution indicated by theidentification. Thus, computing device 110 may evaluate theclassification model based on the probability.

FIG. 9 is a schematic block diagram of example device 900 that can beconfigured to implement an embodiment of content of the presentdisclosure. For example, computing device 110 as shown in FIG. 1 may beimplemented by device 900. As shown in the figure, device 900 includescentral processing unit (CPU) 910 that may perform various appropriateactions and processing according to computer program instructions storedin read-only memory (ROM) 920 or computer program instructions loadedfrom storage unit 980 to random access memory (RAM) 930. In RAM 930,various programs and data required for the operation of device 900 mayalso be stored. CPU 910, ROM 920, and RAM 930 are connected to eachother through bus 940. Input/output (I/O) interface 950 is alsoconnected to bus 940.

A plurality of components in device 900 are coupled to I/O interface950, including: input unit 960, such as a keyboard and a mouse; outputunit 970, such as various types of displays and speakers; storage unit980, such as a magnetic disk and an optical disc; and communication unit990, such as a network card, a modem, and a wireless communicationtransceiver. Communication unit 990 allows device 900 to exchangeinformation/data with other devices over a computer network such as theInternet and/or various telecommunication networks.

Various processes and processing described above, for example, processes300 and 800, can be performed by CPU 910. For example, in someembodiments, processes 300 and 800 may be implemented as a computersoftware program that is tangibly included in a machine-readable medium,for example, storage unit 980. In some embodiments, part or all of thecomputer program may be loaded and/or installed on device 900 via ROM920 and/or communication unit 990. When the computer program is loadedinto RAM 930 and executed by CPU 910, one or more actions of processes300 and 800 described above may be performed.

The present disclosure may be a method, an apparatus, a system, and/or acomputer program product. The computer program product may include acomputer-readable storage medium on which computer-readable programinstructions for performing various aspects of the present disclosureare loaded.

The computer-readable storage medium may be a tangible device capable ofretaining and storing instructions used by an instruction-executingdevice. For example, the computer-readable storage medium may be, but isnot limited to, an electric storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. More specific examples (a non-exhaustive list) of thecomputer-readable storage medium include: a portable computer disk, ahard disk, RAM, ROM, an erasable programmable read-only memory (EPROM orflash memory), a static random access memory (SRAM), a portable compactdisk read-only memory (CD-ROM), a digital versatile disk (DVD), a memorystick, a floppy disk, a mechanical coding device such as a punch card orprotrusions in a groove on which instructions are stored, and anyappropriate combination of the above. The computer-readable storagemedium used here is not construed as transient signals themselves, suchas radio waves or other freely propagating electromagnetic waves,electromagnetic waves propagating through waveguides or othertransmission media (for example, optical pulses through fiber-opticcables), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein can bedownloaded from a computer-readable storage medium to variouscomputing/processing devices, or downloaded to an external computer orexternal storage device via a network such as the Internet, a local areanetwork, a wide area network, and/or a wireless network. The network mayinclude copper transmission cables, optical fiber transmission, wirelesstransmission, routers, firewalls, switches, gateway computers, and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives a computer-readable programinstruction from the network and forwards the computer-readable programinstruction for storage in the computer-readable storage medium in eachcomputing/processing device.

The computer program instructions for performing the operations of thepresent disclosure may be assembly instructions, Instruction SetArchitecture (ISA) instructions, machine instructions, machine-relatedinstructions, microcode, firmware instructions, status setting data, orsource code or object code written in any combination of one or moreprogramming languages, including object-oriented programming languages,such as Java, Smalltalk, and C++, as well as conventional proceduralprogramming languages, such as “C” language or similar programminglanguages. The computer-readable program instructions may be executedentirely on a user computer, executed partly on a user computer,executed as a stand-alone software package, executed partly on a usercomputer while executed partly on a remote computer, or executedentirely on a remote computer or a server. In a case where a remotecomputer is involved, the remote computer can be connected to a usercomputer through any kind of networks, including a local area network(LAN) or a wide area network (WAN), or can be connected to an externalcomputer (for example, connected through the Internet using an Internetservice provider). In some embodiments, an electronic circuit, such as aprogrammable logic circuit, a field programmable gate array (FPGA), or aprogrammable logic array (PLA), can be customized by utilizing statusinformation of the computer-readable program instructions. Theelectronic circuit may execute the computer-readable programinstructions to implement various aspects of the present disclosure.

Various aspects of the present disclosure are described herein withreference to flowcharts and/or block diagrams of the method, theapparatus (the system), and the computer program product according tothe embodiments of the present disclosure. It should be understood thateach block in the flowcharts and/or block diagrams as well as acombination of blocks in the flowcharts and/or block diagrams may beimplemented by using the computer-readable program instructions.

The computer-readable program instructions may be provided to aprocessing unit of a general purpose computer, a special purposecomputer, or other programmable data processing apparatuses to produce amachine, such that the instructions, when executed by the processingunit of the computer or other programmable data processing apparatuses,generate an apparatus for implementing the functions/actions specifiedin one or more blocks in the flowcharts and/or block diagrams. Thecomputer-readable program instructions may also be stored in acomputer-readable storage medium, to cause a computer, a programmabledata processing apparatus, and/or other devices to work in a specificmanner, such that the computer-readable medium storing the instructionsincludes an article of manufacture that contains instructions forimplementing various aspects of the functions/actions specified in oneor more blocks in the flowcharts and/or block diagrams.

The computer-readable program instructions may also be loaded onto acomputer, other programmable data processing apparatuses, or otherdevices, so that a series of operating steps are performed on thecomputer, other programmable data processing apparatuses, or otherdevices to produce a computer-implementing process, so that theinstructions executed on the computer, other programmable dataprocessing apparatuses, or other devices implement the functions/actionsspecified in one or more blocks in the flowcharts and/or block diagrams.

The flowcharts and block diagrams in the accompanying drawings show thearchitectures, functionalities, and operations of possibleimplementations of the system, the method, and the computer programproduct according to a plurality of embodiments of the presentdisclosure. In this regard, each block in the flowcharts or blockdiagrams may represent a module, a program segment, or part of aninstruction, the module, program segment, or part of an instructionincluding one or more executable instructions for implementing specifiedlogical functions. In some alternative implementations, the functionsmarked in the blocks may also occur in an order different from thatmarked in the accompanying drawings. For example, two successive blocksmay actually be performed basically in parallel, or they may beperformed in an opposite order sometimes, depending on the functionsinvolved. It should be further noted that each block in the blockdiagrams and/or flowcharts as well as a combination of the blocks in theblock diagrams and/or flowcharts may be implemented by using a dedicatedhardware-based system for executing specified functions or actions or bya combination of dedicated hardware and computer instructions.

The embodiments of the present disclosure have been described above. Theabove description is illustrative, rather than exhaustive, and is notlimited to the disclosed embodiments. Numerous modifications andalterations are apparent to those of ordinary skill in the art withoutdeparting from the scope and spirit of the illustrated variousembodiments. The selection of terms used herein is intended to bestexplain the principles and practical applications of the embodiments orthe technological improvements to technologies on the market, and tootherwise enable those of ordinary skill in the art to understand theembodiments disclosed herein.

What is claimed is:
 1. A method for processing information, comprising:acquiring a service request record set, each service request record inthe service request record set relating to a problem encountered by auser when the user is provided with a service and a solution to theproblem; constructing a language model based on a first subset in theservice request record set and an initial model, the initial model beingtrained using a predetermined corpus and configured to determine vectorrepresentations of words and sentences in the corpus; and constructing aclassification model based on a second subset in the service requestrecord set and the language model, the classification model beingcapable of determining a solution to a pending problem, and the firstsubset being different from the second subset.
 2. The method of claim 1,wherein each service request record in the service request record setcomprises: an abstract of the problem comprising a plurality of words, adetailed description of the problem comprising a plurality of words, andan identification of the solution to the problem.
 3. The method of claim1, wherein constructing the language model comprises: determining thefirst subset from the service request record set, an identification of asolution in each service request record in the first subset being aninvalid identification; dividing, based on a generation time of eachservice request record in the first subset, the first subset into afirst group of service request records configured to construct thelanguage model and a second group of service request records configuredto evaluate the language model; and constructing the language modelbased on the initial model by using the first group of service requestrecords.
 4. The method of claim 3, wherein constructing the languagemodel based on the initial model comprises: replacing at least one wordin each service request record in the first group of service requestrecords with at least one predetermined word to generate a first groupof replaced service request records; and constructing the language modelby applying the first group of replaced service request records to theinitial model.
 5. The method of claim 3, further comprising: replacingat least one word in each service request record in the second group ofservice request records with at least one predetermined word to generatea second group of replaced service request records; applying the secondgroup of replaced service request records to the language model todetermine at least one prediction result of the at least one word;determining a probability that the at least one prediction resultmatches the at least one word; and evaluating the language model basedon the probability.
 6. The method of claim 1, wherein constructing theclassification model comprises: determining the second subset from theservice request record set, an identification of a solution in eachservice request record in the second subset being a valididentification; dividing, based on a generation time of each servicerequest record in the second subset, the second subset into a thirdgroup of service request records configured to construct theclassification model and a fourth group of service request recordsconfigured to evaluate the classification model; and constructing theclassification model by using the third group of service requestrecords.
 7. The method of claim 6, further comprising: applying thefourth group of service request records to the classification model toobtain a predicted solution; determining a probability that thepredicted solution matches the solution indicated by the identification;and evaluating the classification model based on the probability.
 8. Anelectronic device, comprising: at least one processing unit; and atleast one memory coupled to the at least one processing unit and storinginstructions configured to be executed by the at least one processingunit, wherein when executed by the at least one processing unit, theinstructions cause the device to perform actions comprising: acquiring aservice request record set, each service request record in the servicerequest record set relating to a problem encountered by a user when theuser is provided with a service and a solution to the problem;constructing a language model based on a first subset in the servicerequest record set and an initial model, the initial model being trainedusing a predetermined corpus and configured to determine vectorrepresentations of words and sentences in the corpus; and constructing aclassification model based on a second subset in the service requestrecord set and the language model, the classification model beingcapable of determining a solution to a pending problem, and the firstsubset being different from the second subset.
 9. The device of claim 8,wherein each service request record in the service request record setcomprises: an abstract of the problem comprising a plurality of words, adetailed description of the problem comprising a plurality of words, andan identification of the solution to the problem.
 10. The device ofclaim 8, wherein constructing the language model comprises: determiningthe first subset from the service request record set, an identificationof a solution in each service request record in the first subset beingan invalid identification; dividing, based on a generation time in eachservice request record in the first subset, the first subset into afirst group of service request records configured to construct thelanguage model and a second group of service request records configuredto evaluate the language model; and constructing the language modelbased on the initial model by using the first group of service requestrecords.
 11. The device of claim 10, wherein constructing the languagemodel based on the initial model comprises: replacing at least one wordin each service request record in the first group of service requestrecords with at least one predetermined word to generate a first groupof replaced service request records; and constructing the language modelby applying the first group of replaced service request records to theinitial model.
 12. The device of claim 10, wherein the actions furthercomprise: replacing at least one word in each service request record inthe second group of service request records with at least onepredetermined word to generate a second group of replaced servicerequest records; applying the second group of replaced service requestrecords to the language model to determine at least one predictionresult of the at least one word; determining a probability that the atleast one prediction result matches the at least one word; andevaluating the language model based on the probability.
 13. The deviceof claim 8, wherein constructing the classification module comprises:determining the second subset from the service request record set, anidentification of a solution in each service request record in thesecond subset being a valid identification; dividing, based on ageneration time of each service request record in the second subset, thesecond subset into a third group of service request records configuredto construct the classification model and a fourth group of servicerequest records configured to evaluate the classification model; andconstructing the classification model by using the third group ofservice request records.
 14. The device of claim 13, wherein the actionsfurther comprise: applying the fourth group of service request recordsto the classification model to obtain a predicted solution; determininga probability that the predicted solution matches the solution indicatedby the identification; and evaluating the classification model based onthe probability.
 15. A computer program product tangibly stored in anon-transitory computer-readable medium and comprisingmachine-executable instructions, wherein when executed, themachine-executable instructions cause a machine to perform steps of amethod for processing information, the method comprising: acquiring aservice request record set, each service request record in the servicerequest record set relating to a problem encountered by a user when theuser is provided with a service and a solution to the problem;constructing a language model based on a first subset in the servicerequest record set and an initial model, the initial model being trainedusing a predetermined corpus and configured to determine vectorrepresentations of words and sentences in the corpus; and constructing aclassification model based on a second subset in the service requestrecord set and the language model, the classification model beingcapable of determining a solution to a pending problem, and the firstsubset being different from the second subset.
 16. The computer programproduct of claim 15, wherein each service request record in the servicerequest record set comprises: an abstract of the problem comprising aplurality of words, a detailed description of the problem comprising aplurality of words, and an identification of the solution to theproblem.
 17. The computer program product of claim 15, whereinconstructing the language model comprises: determining the first subsetfrom the service request record set, an identification of a solution ineach service request record in the first subset being an invalididentification; dividing, based on a generation time of each servicerequest record in the first subset, the first subset into a first groupof service request records configured to construct the language modeland a second group of service request records configured to evaluate thelanguage model; and constructing the language model based on the initialmodel by using the first group of service request records.
 18. Thecomputer program product of claim 17, wherein constructing the languagemodel based on the initial model comprises: replacing at least one wordin each service request record in the first group of service requestrecords with at least one predetermined word to generate a first groupof replaced service request records; and constructing the language modelby applying the first group of replaced service request records to theinitial model.
 19. The computer program product of claim 17, furthercomprising: replacing at least one word in each service request recordin the second group of service request records with at least onepredetermined word to generate a second group of replaced servicerequest records; applying the second group of replaced service requestrecords to the language model to determine at least one predictionresult of the at least one word; determining a probability that the atleast one prediction result matches the at least one word; andevaluating the language model based on the probability.
 20. The computerprogram product of claim 15, wherein constructing the classificationmodel comprises: determining the second subset from the service requestrecord set, an identification of a solution in each service requestrecord in the second subset being a valid identification; dividing,based on a generation time of each service request record in the secondsubset, the second subset into a third group of service request recordsconfigured to construct the classification model and a fourth group ofservice request records configured to evaluate the classification model;and constructing the classification model by using the third group ofservice request records.